Moreover, video-paragraphs may sometimes appear to be incoherent linguistically but are actually coherent given the video content. For example, video-paragraph shown in Figure REF (c) looks incoherent as the topics across the sentences shift abruptly. However, note that, this is one of the best possible coherent description one can generate for the video shown in Figure REF (c). This implies that video-paragraph, in this case, is indeed coherent whereas the underlying video is incoherent. In this work, we identify such cases by proposing a discourse representation for videos. We model the coherence of the video-paragraphs by conditioning on coherence of their corresponding videos. We also introduce DisNet, a novel dataset containing discourse annotations of about 3000 videos, to learn a model to automatically infer visual discourse relations. To the best of our knowledge, this is the first work to introduce discourse framework for videos [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
